Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations645
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.6 KiB
Average record size in memory88.2 B

Variable types

Numeric10
Categorical1

Alerts

Bare Nuclei is highly overall correlated with Bland Chromatin and 7 other fieldsHigh correlation
Bland Chromatin is highly overall correlated with Bare Nuclei and 7 other fieldsHigh correlation
Class is highly overall correlated with Bare Nuclei and 8 other fieldsHigh correlation
Clump Thickness is highly overall correlated with Bare Nuclei and 7 other fieldsHigh correlation
Marginal Adhesion is highly overall correlated with Bare Nuclei and 7 other fieldsHigh correlation
Mitoses is highly overall correlated with Class and 1 other fieldsHigh correlation
Normal Nucleoli is highly overall correlated with Bare Nuclei and 7 other fieldsHigh correlation
Single Epithelial Cell Size is highly overall correlated with Bare Nuclei and 7 other fieldsHigh correlation
Uniformity of Cell Shape is highly overall correlated with Bare Nuclei and 7 other fieldsHigh correlation
Uniformity of Cell Size is highly overall correlated with Bare Nuclei and 8 other fieldsHigh correlation
Sample code number has unique values Unique

Reproduction

Analysis started2025-01-04 19:18:41.686358
Analysis finished2025-01-04 19:18:51.507755
Duration9.82 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Sample code number
Real number (ℝ)

Unique 

Distinct645
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1074418.7
Minimum61634
Maximum13454352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-01-04T20:18:51.613365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum61634
5-th percentile411622.4
Q1871549
median1171795
Q31238186
95-th percentile1333987.4
Maximum13454352
Range13392718
Interquartile range (IQR)366637

Descriptive statistics

Standard deviation637262.66
Coefficient of variation (CV)0.59312323
Kurtosis245.40606
Mean1074418.7
Median Absolute Deviation (MAD)104816
Skewness13.459679
Sum6.9300003 × 108
Variance4.061037 × 1011
MonotonicityStrictly increasing
2025-01-04T20:18:51.760704image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13454352 1
 
0.2%
61634 1
 
0.2%
63375 1
 
0.2%
76389 1
 
0.2%
95719 1
 
0.2%
1350423 1
 
0.2%
1350319 1
 
0.2%
1348851 1
 
0.2%
1347943 1
 
0.2%
1347749 1
 
0.2%
Other values (635) 635
98.4%
ValueCountFrequency (%)
61634 1
0.2%
63375 1
0.2%
76389 1
0.2%
95719 1
0.2%
128059 1
0.2%
142932 1
0.2%
144888 1
0.2%
145447 1
0.2%
160296 1
0.2%
167528 1
0.2%
ValueCountFrequency (%)
13454352 1
0.2%
8233704 1
0.2%
1371920 1
0.2%
1371026 1
0.2%
1369821 1
0.2%
1368882 1
0.2%
1368273 1
0.2%
1368267 1
0.2%
1365328 1
0.2%
1365075 1
0.2%

Clump Thickness
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5844961
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-01-04T20:18:51.876000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8236592
Coefficient of variation (CV)0.61591484
Kurtosis-0.70065714
Mean4.5844961
Median Absolute Deviation (MAD)2
Skewness0.52835969
Sum2957
Variance7.9730512
MonotonicityNot monotonic
2025-01-04T20:18:51.973284image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 127
19.7%
1 120
18.6%
3 97
15.0%
4 74
11.5%
10 69
10.7%
8 46
 
7.1%
2 44
 
6.8%
6 33
 
5.1%
7 22
 
3.4%
9 13
 
2.0%
ValueCountFrequency (%)
1 120
18.6%
2 44
 
6.8%
3 97
15.0%
4 74
11.5%
5 127
19.7%
6 33
 
5.1%
7 22
 
3.4%
8 46
 
7.1%
9 13
 
2.0%
10 69
10.7%
ValueCountFrequency (%)
10 69
10.7%
9 13
 
2.0%
8 46
 
7.1%
7 22
 
3.4%
6 33
 
5.1%
5 127
19.7%
4 74
11.5%
3 97
15.0%
2 44
 
6.8%
1 120
18.6%

Uniformity of Cell Size
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2542636
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-01-04T20:18:52.071251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0878194
Coefficient of variation (CV)0.9488535
Kurtosis-0.089989774
Mean3.2542636
Median Absolute Deviation (MAD)0
Skewness1.1548188
Sum2099
Variance9.5346285
MonotonicityNot monotonic
2025-01-04T20:18:52.173016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 337
52.2%
10 65
 
10.1%
3 51
 
7.9%
2 44
 
6.8%
4 40
 
6.2%
5 30
 
4.7%
8 28
 
4.3%
6 25
 
3.9%
7 19
 
2.9%
9 6
 
0.9%
ValueCountFrequency (%)
1 337
52.2%
2 44
 
6.8%
3 51
 
7.9%
4 40
 
6.2%
5 30
 
4.7%
6 25
 
3.9%
7 19
 
2.9%
8 28
 
4.3%
9 6
 
0.9%
10 65
 
10.1%
ValueCountFrequency (%)
10 65
 
10.1%
9 6
 
0.9%
8 28
 
4.3%
7 19
 
2.9%
6 25
 
3.9%
5 30
 
4.7%
4 40
 
6.2%
3 51
 
7.9%
2 44
 
6.8%
1 337
52.2%

Uniformity of Cell Shape
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3348837
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-01-04T20:18:52.271698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.005085
Coefficient of variation (CV)0.90110638
Kurtosis-0.181504
Mean3.3348837
Median Absolute Deviation (MAD)1
Skewness1.0803957
Sum2151
Variance9.0305359
MonotonicityNot monotonic
2025-01-04T20:18:52.372141image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 307
47.6%
2 57
 
8.8%
3 56
 
8.7%
10 56
 
8.7%
4 43
 
6.7%
5 33
 
5.1%
7 30
 
4.7%
8 28
 
4.3%
6 28
 
4.3%
9 7
 
1.1%
ValueCountFrequency (%)
1 307
47.6%
2 57
 
8.8%
3 56
 
8.7%
4 43
 
6.7%
5 33
 
5.1%
6 28
 
4.3%
7 30
 
4.7%
8 28
 
4.3%
9 7
 
1.1%
10 56
 
8.7%
ValueCountFrequency (%)
10 56
 
8.7%
9 7
 
1.1%
8 28
 
4.3%
7 30
 
4.7%
6 28
 
4.3%
5 33
 
5.1%
4 43
 
6.7%
3 56
 
8.7%
2 57
 
8.8%
1 307
47.6%

Marginal Adhesion
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9255814
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-01-04T20:18:52.468457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.9235677
Coefficient of variation (CV)0.99931171
Kurtosis0.66566178
Mean2.9255814
Median Absolute Deviation (MAD)0
Skewness1.4252717
Sum1887
Variance8.5472483
MonotonicityNot monotonic
2025-01-04T20:18:52.569330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 362
56.1%
2 55
 
8.5%
10 55
 
8.5%
3 54
 
8.4%
4 32
 
5.0%
8 24
 
3.7%
5 23
 
3.6%
6 22
 
3.4%
7 13
 
2.0%
9 5
 
0.8%
ValueCountFrequency (%)
1 362
56.1%
2 55
 
8.5%
3 54
 
8.4%
4 32
 
5.0%
5 23
 
3.6%
6 22
 
3.4%
7 13
 
2.0%
8 24
 
3.7%
9 5
 
0.8%
10 55
 
8.5%
ValueCountFrequency (%)
10 55
 
8.5%
9 5
 
0.8%
8 24
 
3.7%
7 13
 
2.0%
6 22
 
3.4%
5 23
 
3.6%
4 32
 
5.0%
3 54
 
8.4%
2 55
 
8.5%
1 362
56.1%

Single Epithelial Cell Size
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.296124
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-01-04T20:18:52.666324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2438461
Coefficient of variation (CV)0.68075292
Kurtosis1.888946
Mean3.296124
Median Absolute Deviation (MAD)0
Skewness1.6370874
Sum2126
Variance5.0348452
MonotonicityNot monotonic
2025-01-04T20:18:52.764603image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 346
53.6%
3 70
 
10.9%
4 47
 
7.3%
6 40
 
6.2%
1 39
 
6.0%
5 39
 
6.0%
10 30
 
4.7%
8 20
 
3.1%
7 12
 
1.9%
9 2
 
0.3%
ValueCountFrequency (%)
1 39
 
6.0%
2 346
53.6%
3 70
 
10.9%
4 47
 
7.3%
5 39
 
6.0%
6 40
 
6.2%
7 12
 
1.9%
8 20
 
3.1%
9 2
 
0.3%
10 30
 
4.7%
ValueCountFrequency (%)
10 30
 
4.7%
9 2
 
0.3%
8 20
 
3.1%
7 12
 
1.9%
6 40
 
6.2%
5 39
 
6.0%
4 47
 
7.3%
3 70
 
10.9%
2 346
53.6%
1 39
 
6.0%

Bare Nuclei
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.655814
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-01-04T20:18:52.862837image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q37
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6804821
Coefficient of variation (CV)1.0067476
Kurtosis-0.9303434
Mean3.655814
Median Absolute Deviation (MAD)0
Skewness0.92146991
Sum2358
Variance13.545948
MonotonicityNot monotonic
2025-01-04T20:18:52.963501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 369
57.2%
10 129
 
20.0%
5 30
 
4.7%
3 28
 
4.3%
2 28
 
4.3%
8 22
 
3.4%
4 18
 
2.8%
9 9
 
1.4%
7 8
 
1.2%
6 4
 
0.6%
ValueCountFrequency (%)
1 369
57.2%
2 28
 
4.3%
3 28
 
4.3%
4 18
 
2.8%
5 30
 
4.7%
6 4
 
0.6%
7 8
 
1.2%
8 22
 
3.4%
9 9
 
1.4%
10 129
 
20.0%
ValueCountFrequency (%)
10 129
 
20.0%
9 9
 
1.4%
8 22
 
3.4%
7 8
 
1.2%
6 4
 
0.6%
5 30
 
4.7%
4 18
 
2.8%
3 28
 
4.3%
2 28
 
4.3%
1 369
57.2%

Bland Chromatin
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5364341
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-01-04T20:18:53.065738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4521488
Coefficient of variation (CV)0.69339587
Kurtosis0.024438557
Mean3.5364341
Median Absolute Deviation (MAD)1
Skewness1.0346854
Sum2281
Variance6.0130338
MonotonicityNot monotonic
2025-01-04T20:18:53.162031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 153
23.7%
3 152
23.6%
1 128
19.8%
7 71
11.0%
4 40
 
6.2%
5 34
 
5.3%
8 28
 
4.3%
10 19
 
2.9%
9 10
 
1.6%
6 10
 
1.6%
ValueCountFrequency (%)
1 128
19.8%
2 153
23.7%
3 152
23.6%
4 40
 
6.2%
5 34
 
5.3%
6 10
 
1.6%
7 71
11.0%
8 28
 
4.3%
9 10
 
1.6%
10 19
 
2.9%
ValueCountFrequency (%)
10 19
 
2.9%
9 10
 
1.6%
8 28
 
4.3%
7 71
11.0%
6 10
 
1.6%
5 34
 
5.3%
4 40
 
6.2%
3 152
23.6%
2 153
23.7%
1 128
19.8%

Normal Nucleoli
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9937984
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-01-04T20:18:53.265024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.132175
Coefficient of variation (CV)1.0462211
Kurtosis0.16658864
Mean2.9937984
Median Absolute Deviation (MAD)0
Skewness1.3202562
Sum1931
Variance9.8105205
MonotonicityNot monotonic
2025-01-04T20:18:53.359248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 396
61.4%
10 61
 
9.5%
3 41
 
6.4%
2 35
 
5.4%
8 24
 
3.7%
6 21
 
3.3%
5 18
 
2.8%
4 17
 
2.6%
7 16
 
2.5%
9 16
 
2.5%
ValueCountFrequency (%)
1 396
61.4%
2 35
 
5.4%
3 41
 
6.4%
4 17
 
2.6%
5 18
 
2.8%
6 21
 
3.3%
7 16
 
2.5%
8 24
 
3.7%
9 16
 
2.5%
10 61
 
9.5%
ValueCountFrequency (%)
10 61
 
9.5%
9 16
 
2.5%
8 24
 
3.7%
7 16
 
2.5%
6 21
 
3.3%
5 18
 
2.8%
4 17
 
2.6%
3 41
 
6.4%
2 35
 
5.4%
1 396
61.4%

Mitoses
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6325581
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.2 KiB
2025-01-04T20:18:53.453130image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile6
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7753996
Coefficient of variation (CV)1.0874955
Kurtosis11.448962
Mean1.6325581
Median Absolute Deviation (MAD)0
Skewness3.4081605
Sum1053
Variance3.1520439
MonotonicityNot monotonic
2025-01-04T20:18:53.552845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 527
81.7%
2 35
 
5.4%
3 31
 
4.8%
10 14
 
2.2%
4 12
 
1.9%
7 9
 
1.4%
8 8
 
1.2%
5 6
 
0.9%
6 3
 
0.5%
ValueCountFrequency (%)
1 527
81.7%
2 35
 
5.4%
3 31
 
4.8%
4 12
 
1.9%
5 6
 
0.9%
6 3
 
0.5%
7 9
 
1.4%
8 8
 
1.2%
10 14
 
2.2%
ValueCountFrequency (%)
10 14
 
2.2%
8 8
 
1.2%
7 9
 
1.4%
6 3
 
0.5%
5 6
 
0.9%
4 12
 
1.9%
3 31
 
4.8%
2 35
 
5.4%
1 527
81.7%

Class
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
benign
410 
malignant
235 

Length

Max length9
Median length6
Mean length7.0930233
Min length6

Characters and Unicode

Total characters4575
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbenign
2nd rowmalignant
3rd rowmalignant
4th rowmalignant
5th rowbenign

Common Values

ValueCountFrequency (%)
benign 410
63.6%
malignant 235
36.4%

Length

2025-01-04T20:18:53.661391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-04T20:18:53.769859image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
benign 410
63.6%
malignant 235
36.4%

Most occurring characters

ValueCountFrequency (%)
n 1290
28.2%
g 645
14.1%
i 645
14.1%
a 470
 
10.3%
b 410
 
9.0%
e 410
 
9.0%
m 235
 
5.1%
l 235
 
5.1%
t 235
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4575
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 1290
28.2%
g 645
14.1%
i 645
14.1%
a 470
 
10.3%
b 410
 
9.0%
e 410
 
9.0%
m 235
 
5.1%
l 235
 
5.1%
t 235
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4575
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 1290
28.2%
g 645
14.1%
i 645
14.1%
a 470
 
10.3%
b 410
 
9.0%
e 410
 
9.0%
m 235
 
5.1%
l 235
 
5.1%
t 235
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4575
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 1290
28.2%
g 645
14.1%
i 645
14.1%
a 470
 
10.3%
b 410
 
9.0%
e 410
 
9.0%
m 235
 
5.1%
l 235
 
5.1%
t 235
 
5.1%

Interactions

2025-01-04T20:18:50.316498image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-04T20:18:44.973097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-04T20:18:46.734286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T20:18:47.613145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T20:18:48.464295image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T20:18:49.449108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T20:18:50.413461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-04T20:18:49.726775image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-01-04T20:18:49.233420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-04T20:18:50.236497image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-04T20:18:53.844992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Bare NucleiBland ChromatinClassClump ThicknessMarginal AdhesionMitosesNormal NucleoliSample code numberSingle Epithelial Cell SizeUniformity of Cell ShapeUniformity of Cell Size
Bare Nuclei1.0000.6840.8370.5940.6960.4740.651-0.1220.6790.7450.762
Bland Chromatin0.6841.0000.7990.5410.6300.3830.668-0.0950.6460.7020.733
Class0.8370.7991.0000.7320.7360.5110.7610.0000.7800.8520.869
Clump Thickness0.5940.5410.7321.0000.5370.4160.570-0.0210.5710.6670.667
Marginal Adhesion0.6960.6300.7360.5371.0000.4470.634-0.0540.6710.7110.750
Mitoses0.4740.3830.5110.4160.4471.0000.498-0.0770.4750.4650.507
Normal Nucleoli0.6510.6680.7610.5700.6340.4981.000-0.0740.7060.7250.757
Sample code number-0.122-0.0950.000-0.021-0.054-0.077-0.0741.000-0.081-0.056-0.040
Single Epithelial Cell Size0.6790.6460.7800.5710.6710.4750.706-0.0811.0000.7550.788
Uniformity of Cell Shape0.7450.7020.8520.6670.7110.4650.725-0.0560.7551.0000.894
Uniformity of Cell Size0.7620.7330.8690.6670.7500.5070.757-0.0400.7880.8941.000

Missing values

2025-01-04T20:18:51.210288image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-04T20:18:51.402744image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Sample code numberClump ThicknessUniformity of Cell SizeUniformity of Cell ShapeMarginal AdhesionSingle Epithelial Cell SizeBare NucleiBland ChromatinNormal NucleoliMitosesClass
061634543121231benign
1633759126410772malignant
2763891047228611malignant
39571961010108107107malignant
4128059111125511benign
5142932761053109102malignant
6144888810108510781malignant
7145447844129331malignant
8160296588105108103malignant
9167528411121361benign
Sample code numberClump ThicknessUniformity of Cell SizeUniformity of Cell ShapeMarginal AdhesionSingle Epithelial Cell SizeBare NucleiBland ChromatinNormal NucleoliMitosesClass
6351365075414121111benign
6361365328112121211benign
6371368267511121111benign
6381368273111121111benign
6391368882211121111benign
64013698211010101051010107malignant
64113710265101010410563malignant
6421371920511121321benign
6438233704411111211benign
64413454352113121211benign